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The purpose of this study was to develop a 40-criteria agility assessment model and explore its practical feasibility in an industrial scenario.

Agile manufacturing (AM) principles enable organizations to understand customer needs and incorporate the necessary changes in product- and processes-oriented approaches. In this research study, a 40-criteria agility assessment model was developed. The agility assessment model was subjected to investigation in an Indian relays manufacturing organization.

The research study indicates that the organization is agile. Besides computing agility level, the gaps across agile criteria have been identified and actions for agility improvement were subjected to implementation in the case organization.

The 40-criteria agility assessment model was subjected to investigation in a single manufacturing organization. In future, more number of studies could be conducted.

To acquire agile characteristics, modern organizations should assess the agility level at which they operate. In this context, the agility assessment model was developed.

The agility assessment tool presented in this paper consists of 40 agile criteria, which are well supported by the research findings reported in literature. Hence, the developed 40-criteria agile model is original and novel.

The purpose of this paper is to benchmark the assessment approaches of agility in a manufacturing organization.

The criteria for agility assessment were identified comprehensively based on literature review. The agility assessment was done using Multi Grade Fuzzy and Fuzzy logic approaches, and the results were benchmarked.

Based on Multi Grade Fuzzy approach, the agility index was found to be 6.6; Fuzzy logic approach reveals the agility index as (5.37, 6.91, 8.45) which indicated the case organization is agile. The gaps were identified from both the approaches and the results were corroborated.

In the present study, Multi Grade Fuzzy and Fuzzy logic approaches were only benchmarked. Also, the benchmarking exercise was done only in one manufacturing organization.

The benchmarking study was conducted in a manufacturing organization. The practitioners’ views were gathered and they were involved in the study to substantiate the practical validity.

The benchmarking study between two approaches for agility assessment was found to be original and adds value to the agility assessment field.

An evolutionary transformation of business environment, with change as a main characteristic, is taking place. Manufacturing companies, even those operating in relatively stable conditions with good market positions, are facing rapid and often unanticipated changes in their business environment. Agile manufacturing is proposed in response to the circumstances as a solution and is perceived as a vital characteristic that manufacturing companies need to have in order to maintain their competitive advantages in the new order of world business. Each company will respond in a specific and different way to the changing circumstances by deploying its own agile characteristics. Agility in manufacturing may be achieved through the implementation and integration of appropriate practices which provide the required abilities for a company to respond properly to changes. Based on this concept, a methodology for achieving agility in manufacturing organisations is developed. The methodology is applied in two manufacturing companies and data collected from the applications are used to validate the methodology. This paper provides a brief summary of the methodology and details its implementation and validation in the two case study companies. Practices are proposed to support the achievement of agility in the two organisations.

The purpose of this paper is to determine if companies in the modular and offsite construction (MOC) industry are agile or not and its level of application for agility principles, which allows for quick responses to the increasingly dynamic nature of industry environments.

This paper proposes an agility assessment framework for MOC that uses 48 assessment attributes organized into four categories: metrics, drivers, enablers and capabilities. A questionnaire approach was used to disseminate the framework globally in 19 countries and synthesize its relevance to the MOC industry. The questionnaire had 55 complete responses, majority of respondents work in managerial positions for MOC manufacturing facilities and onsite general contractors.

It was found that the lowest metric score for adapting to change was for cost since controlling cost would be difficult for any changes required after the design freeze stage. The top agility driver was found to be the need to respond to the wide variety of customer expectations, while the lowest driver was the existence of competing priorities. The top agility enabler was vendor partnership, which can be related to current postpandemic supply chain disruptions. Regarding technological capabilities, Europe and the USA acquired better scores compared to Asia, Latin America and Africa.

This study contributes to the MOC body of knowledge by creating an agility assessment tool for MOC firms to analyze their agile approach and environment, identifying the preliminary importance of agility assessment attributes and determining significant agile differences between the main MOC industry groups.

The purpose of this study is to critically examine the scholarly articles associated with the diverse aspects of supply chain agility (SCA). The review highlights research insights, existing gaps and future research directions that can help academicians and practitioners gain a comprehensive understanding of SCA.

The present study has adopted author co-citation analysis as the research methodology, with a view to thoroughly investigating the good-quality articles related to SCA that have been published over a period of 22 years (1999-2020). In this study, 126 research papers on SCA – featuring diverse aspects of agility – from various reputed journals have been examined, analysed and assimilated.

The salient findings of this research are, namely, agility is different from other similar concepts, such as flexibility, leanness, adaptability and resilience; of the 13 dimensions of agility discussed in the literature, the prominent ones are quickness, responsiveness, competency and flexibility; literature related to SCA can be categorised as related to modelling the enablers, agility assessment, agility implementation, leagility and agility maximisation. This research proposes a more practical definition and framework for SCA. The probable areas for future research are, namely, impediments to agility, effective approaches to agility assessment, cost-benefit trade-offs to be considered whilst implementing agility, empirical research to validate the framework and SCA in the domain of healthcare and disaster relief supply chains.

This paper provides substantial insights to practitioners who primarily focus on measuring and implementing agility in the supply chain. The findings of this study will help the supply chain manager gain a better idea about how to become competitive in today’s dynamic and turbulent business environment.

The originality of this study is in: comprehensively identifying the various issues related to SCA, such as related concepts, definitions, dimensions and different categories of studies covered in literature, proposing a new definition and framework for SCA and identifying potential areas for future research, to provide deeper insights into the subject and highlight areas for future research.

The purpose of this paper is to propose a framework to assess the agility of manufacturing companies.

Particularly, three supply chain logistic drivers (facility, transportation and inventory) along with three cross-functional drivers (information, sourcing and pricing) are selected as the main sets to classify all required activities of agility. In addition, supply chain contracts, as an important indicator of supply chain agility, is also considered to categorize the activities. These activities are ranked using an (AHP) and then categorized based on the major perspectives of agility. Finally, using a cycle view of supply chain, the developed activities are categorized as the major policies of supply chain’s echelons.

This study developed a framework to evaluate the agility of manufacturing companies. Operational activities of agile supply chain strategy (ASCS) in addition to supply chain contracts are determined and categorized with regard to supply chain drivers.

This study contributes to recognizing, ranking and classifying the operational activities of ASCS with regard to logistics and cross-functional drivers of supply chain. In addition, this study considers the supply chain contracts in conjunction with supply chain drivers. From the theoretic and methodological features, to the best of authors’ knowledge, this study contributes to offer new insights to this area as no similar research has been conducted before.

The purpose of this research was to construct a quantitative, objective metric for agility performance that assesses agility as a performance outcome, capturing both organizational success and environmental turbulence, and applicable to manufacturing organizations of all types.

The agility performance metric was developed by creating a theoretical model and then operationalizing the model through literature review, case studies, and pilot survey data. It was subsequently refined, based on input from an expert panel and survey responses.

The agility performance metric is demonstrated using data from four manufacturing plants, which represent the four possible combinations of success and turbulence.

The agility metric developed is consistent with the theoretical model, as well as empirical evidence from the demonstration companies. Further validation of the metric is necessary to fully establish this approach as a valid and reliable assessment tool.

This approach could be used by manufacturing managers to get a “snapshot” of an organization's agility performance level and to systematically consider the influence of environmental entities.

Consideration of agility as a performance outcome, rather than a structural or operational characteristic is a novel approach. The resulting quantitative performance index, which closely matches the theoretical definition of agility, is applicable to any type of manufacturing organization, can be used to make direct comparisons between manufacturers in different industries, and will automatically be updated over time as success is relative to industry medians.

The purpose of this paper is to develop a framework to manage the strategy to counter disruptive innovation by identifying stronger attributes and key strategic areas for improvements in an engineering services context with less freedom to orchestrate product innovation. The authors also validate the combined use of multi-grade fuzzy, Importance-Performance Analysis (IPA) and gap analysis in developing the strategy to mitigate turbulence.

The paper presents a framework using a combination of Dynamic Capabilities and Organizational Agility to develop the strategy. The study uses data gathered from a 60-member engineering project team working on an Aircraft Engine Controller design to identify 50 attributes and the related importance and performance ratings. The research study is designed using a three-tier approach. First, multi-grade fuzzy is utilized to measure the overall firm response agility index. In the second step, the IPA is used to analyze the strength and weaknesses of the firm and to identify the attributes where the firm needs to focus. In the past step, gap analysis is used to prioritize the identified attributes. The findings are validated by panel discussions with a different group of experts from the project team and action points were arrived at.

This research work finds that the firms’ response agility index of 6.97 is comparable to that of the Original Equipment Manufacturers (OEMs) from previous literature, but still needs a better agility score to effectively counter turbulence from disruptive innovations. It also identifies seven key areas for improvement and their relative priority to effectively improve the response agility by utilizing the least number of resources, the reasons for the gap and the mitigation strategy to close the gap. The findings highlight a few key differences between an engineering services firm in comparison to OEMs and Software Services firms.

The findings help the practitioners with a comparative agility score of an engineering services firm, and an effective way of measuring agility, identify key focus areas and prioritize the actions using a simple set of data that could be collected regularly to keep track of the improvements. The provided strategic framework to improve the agility score can be used for continuous improvement.

This paper contributes to the existing literature by developing a framework for outsourcing services companies to cope with the turbulence by using dynamic capabilities and organizational agility. It also adds to the literature by extending the validity of IPA and gap analysis in making strategic decisions in an industrial set-up.

The purpose of this paper is to investigate the influence of decision-makers’ (DM) risk bearing attitudes and the effect of the decision-making environment on estimating the overall degree of agility of an organization. The present study explores an extended agility model in a specific organization's hierarchy and reflects how decision-making attitudes alter an organizational agility scenario.

The concept of fuzzy logic has been explored in this paper. Based on DMs’ linguistic judgments, a fuzzy appropriateness rating as well as fuzzy priority weights have been determined for different levels of agile system hierarchy. Using a multi-grade fuzzy approach the overall agility index has been determined. The concept of fuzzy numbers ranking has been explored to show the effect of decision-making attitudes on agility estimations.

Decision-making attributes, e.g. the category of DM (neutral, risk-averse and risk-taking), affect the quantitative evaluation of the overall agility degree, which is correlated with a predefined agility measurement scale.

This study explores a triangular fuzzy membership function to express DMs’ linguistic judgments as fuzzy representations. Apart from triangular fuzzy numbers, trapezoidal and Gaussian fuzzy numbers may also be used for agility evaluation. The model may be used in other agile industries for benchmarking and selection of the best approach.

Selecting the right decision-making group to compute and analyze the agility level for a particular organization is an important managerial decision. In the case of benchmarking of various agile enterprises the decision-making group bearing the same attitude should be utilized.

Agile system modeling and development of agility appraisement platforms have been attempted by previous researchers while the influence of DMs’ risk bearing attitudes, and the effect of the decision-making environment on estimating the overall degree of agility, have rarely been studied. In this context, the authors explore an exhaustive agility model for implementing in a case study and reveal how decision-making attitudes alter organizational agility scenarios.

A survey study was conducted in seven best practice organizations in the field of talent management. By cross‐checking their existing high potential lists, the authors aimed to examine to which extent assessments of learning agility were able to predict being identified as a high potential or not above and beyond a baseline prediction by job performance. Furthermore, they aimed to investigate whether learning agility increased with career variety.

The study had a case‐control design, comparing supervisor ratings of employees recently identified as high potentials (n=32) with supervisor ratings of a carefully matched control group of non‐high potentials (n=31).

Learning agility (mediated by job content on‐the‐job learning) was found to be a better predictor of being identified as a high potential than job performance. Career variety was found to be positively associated to learning agility.

This study's design did not allow for the demonstration of causal effects. Longitudinal studies are needed to further clarify the causality of these findings and their implications for organizational performance.

Organizations should do well to incorporate measures of learning agility into their high potential identification and development processes. Furthermore, they need to reflect on how HRM practices might enhance their high potentials' career variety and commitment.

The current study responds to urgent calls in the literature for more empirical research on the identification and development of high potentials, as well as on career variety.